Former contre les discriminations (ethno)culturelles
Bibliographic record
Abstract
En France comme ailleurs, l’École est rappelée à sa mission d’éducation au vivre ensemble et à la démocratie. Dans ce contexte, la lutte contre la discrimination – ici ethnoculturelle – s’est peu à peu constituée comme objet de formation. Pourtant, si une palette d’outils et de nouveaux contenus voient le jour, ceux-ci restent éparpillés et peu lisibles. Inégalement appropriée par les acteurs, cette dimension de la formation est aussi trop peu arrimée à la recherche. Ce dossier entend contribuer à structurer la réflexion dans ce domaine à partir d’une double entrée. D’une part, il tire parti de la recherche sur les phénomènes de discrimination et sur les processus associés (ségrégation, biais d’évaluation ou d’orientation, etc.) afin d’identifier des objets et des leviers de formation. D’autre part, il analyse de manière réflexive des dispositifs de formation dédiés afin d’en dégager les écueils et les potentialités. Les recherches présentées recouvrent quatre contextes : suisse francophone, belge francophone, québécois, français. La comparaison révèle la forte indexicalité de ces questions, tant dans la manière de désigner les groupes discriminés et de construire les problèmes que dans la place occupée par cet objet à l’agenda des politiques éducatives et de formation. In fine, le dossier invite à prendre à bras le corps le débat sur les discriminations ethnoculturelles et à en faire un objet de réflexivité collective, en tenant spécifiquement compte des contextes locaux. In France as elsewhere, schools are reminded of their mission to educate people for coexistence and democracy. In this context, the fight against ethnocultural discrimination has gradually become an object of teacher training. However, while new training content and a range of tools are emerging, they remain scattered and opaque. Unevenly adopted by stakeholders, this dimension of teacher training is also insufficiently linked to research. This issue aims to contribute to structuring the reflection in this area from two complementary perspectives. On the one hand, it takes advantage of research on discrimination and its accompanying school processes (segregation, evaluation or orientation bias, etc.) to identify training objects and levers. On the other hand, it reflexively scrutinises dedicated training methods in order to identify their pitfalls and potentialities. The research presented covers four contexts: French-speaking Switzerland, French-speaking Belgium, Quebec, and France. The comparison reveals the strong indexicality of these questions, both in the way in which discriminated groups are identified and problems are constructed, and in the place this subject occupies on the agenda of education and training policies. To sum up, the dossier invites to tackle the debate on ethnocultural discrimination head-on and make it an object of collective reflexivity, taking specific account of local contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".